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Measuring clutch performance

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  • dlirag@hotmail.com
    Is it possible now to quantify the level of a team or player s clutch ability with the methods available to this group?
    Message 1 of 19 , Nov 29, 2001
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      Is it possible now to quantify the level of a team or player's clutch
      ability with the methods available to this group?
    • NYFan@aol.com
      First you d have to figure out what clutch is... is clutch hitting a big shot that changes momentum in the 2nd quarter? is clutch hitting a game winning shot,
      Message 2 of 19 , Nov 29, 2001
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        First you'd have to figure out what clutch is... is clutch hitting a big shot that changes momentum in the 2nd quarter? is clutch hitting a game winning shot, grabbing a big rebound to solidify a win? I don't really think you can quantify clutch, without physically watching a game 2 or 3 times. Determining momentum shifts, (ending a run with a huge three, that's clutch) determining when a game is really close, and when it's not (games that end up with 6-7 point differentials often time were within 2 with under 5 mins to go, or were huge differentials, so you can't just tell by the end score whether it was a close game), and figuring out when a team just makes a break down that allows you to make a play that seems big (that college game, where a guy's knees buckled due to dehydration or something, and the dribbler drove right by him for an easy shot or layup or something... is that clutch, or just a defensive breakdown?). I think you could probably come up with a calculation of clutchness, both team wise and player wise, but I don't think it'd be very accurate, because a lot of those things are things that happen spontaneously, and thus you really can't set it at a percentage (times a player came up with a momentum change, divided by times he didn't, just wouldn't work), and a sum total could be a little accurate, but what if your team is really good and you just blow teams out on a regular basis by building up leads early in the game and holding onto them. I don't really see an easy way to measure this, without watching game tapes and drawing opinions, not statistics.

        ~Ray

        In a message dated 11/30/01 12:18:00 AM Eastern Standard Time, dlirag@... writes:


        Is it possible now to quantify the level of a team or player's clutch
        ability with the methods available to this group?


      • igor eduardo k�pfer
        ... shot ... First of all, hi group. I m new here. Secondly, I ve been trying to come up with a definition of a clutch shooter that _is_ quantifiable. Here
        Message 3 of 19 , Nov 29, 2001
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          : > Is it possible now to quantify the level of a team or player's clutch
          : > ability with the methods available to this group?
          :
          : : First you'd have to figure out what clutch is... is clutch hitting a big
          shot
          : that changes momentum in the 2nd quarter? is clutch hitting a game winning
          : shot, grabbing a big rebound to solidify a win? I don't really think you can
          : quantify clutch, without physically watching a game 2 or 3 times.

          First of all, hi group. I'm new here.

          Secondly, I've been trying to come up with a definition of a "clutch shooter"
          that _is_ quantifiable. Here are a few ideas:

          1. a. A clutch shooter is one who shooting % rises in the 4thQ
          b. A clutch shooter is one who shooting % rises in the last 5 minutes.

          2. A clutch shooter is one who's shooting % rises when his team is within 10
          points of the other.

          3. A clutch shooter is one who shooting % rises with less than 5 seconds left on
          the shot clock.

          I know this doesn't cover all the meanings in the word "clutch", but I think
          these are still useful. Would you expect a good clutch shooter to have his
          shooting % to _drop_ in the last quarter?

          In any case, #1 is pretty easy to calculate using the game logs. #2 and #3 might
          be impossible without actually watching the games.
        • NYFan@aol.com
          Well, being the pain in the butt I am, here is my argument to what you said. The last 5 minutes of a game can be clutch, or it can be garbage time. Garbage
          Message 4 of 19 , Nov 29, 2001
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            Well, being the pain in the butt I am, here is my argument to what you said. The last 5 minutes of a game can be clutch, or it can be garbage time. Garbage time = no defense, = higher FG%. I think if you did things the way you wrote them up, you'd see guys who you wouldn't expect up there because their team is involved in a lot of blow outs, and guys see their only time in games at the end of games, where limited defense is being played more so than a lot of good players probably stand out at that time with tougher defense. Since most games are generally played within 10 points of each other, that really doesn't seem to eliminate much. Granted there is a lot of time when the differential is larger also, but I think 10 points is a bit too broad almost. I think to get the best reference, it would have to be differential in relationship to time... within 10 points during the 2nd and 3rd quarters, and within 6 points during the 1st and 4th quarters. The 2nd and 3rd quarters are usually where teams make their big runs, and momentum seems to be very shifty over long runs (4th quarters see a lot of 4-6 point streaks, 2nd and 3rd quarters i think see more 9-12 point streaks) and thus the differentials tend to sway largely during those quarters, where as the 1st and 4th tend to be a bit more methodical, as each team tries to set a pace, and as a result a smaller margin of error is created, especially in the fourth quarter. #3 is hard to disagree with. Also, I may expect a clutch shooters' shooting percentage not to rise during the 4th quarter. 4th quarters are typically defensive (I would love to see an average break down of scoring per quarter, I bet the 4th quarter is probably 4-5 points lower than any other quarter) orientated quarters, as a result maintaining a FG% may be equivalent to increasing a FG%. Also, a FG% may drop, if a coach feels that a player is clutch, and thus they want them to take more shots, tougher shots, and bad shots because they want the ball in that players hands. Very few would argue against Iverson being a clutch player, and yet I've seen him shoot poorly by trying to do too much because nobody else in the lineup is capable of doing anything.

            ~Ray

            In a message dated 11/30/01 1:05:11 AM Eastern Standard Time,
            edkupfer@... writes:


            First of all, hi group. I'm new here.

            Secondly, I've been trying to come up with a definition of a "clutch shooter"
            that _is_ quantifiable. Here are a few ideas:

            1. a.  A clutch shooter is one who shooting % rises in the 4thQ
               b.  A clutch shooter is one who shooting % rises in the last 5 minutes.

            2. A clutch shooter is one who's shooting % rises when his team is within 10
            points of the other.

            3. A clutch shooter is one who shooting % rises with less than 5 seconds left on
            the shot clock.

            I know this doesn't cover all the meanings in the word "clutch", but I think
            these are still useful. Would you expect a good clutch shooter to have his
            shooting % to _drop_ in the last quarter?

            In any case, #1 is pretty easy to calculate using the game logs. #2 and #3 might
            be impossible without actually watching the games.


          • Mike Goodman
            Clutch play by a team is often reflected in the percent of close games they win. It might also include the quality of the opponent, and the situation:
            Message 5 of 19 , Nov 30, 2001
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              "Clutch" play by a team is often reflected in the percent of close
              games they win. It might also include the quality of the opponent,
              and the situation: vying for playoff position, or actually in the
              playoffs.

              --- In APBR_analysis@y..., dlirag@h... wrote:
              > Is it possible now to quantify the level of a team or player's
              clutch
              > ability with the methods available to this group?

              Individual clutch play certainly makes or breaks a reputation,
              particularly if one's performance leads to playoff wins and more
              opportunity to prove oneself. I have seen some very clutch
              performances in losing efforts, however.

              The idea of creating a momentum change in the 2nd or 3rd quarter is
              curious to me. Only in retrospect can a momentum-change be seen.
              The guy who heaves up a 30-foot shot to the dismay of his coach, may
              later be called Mr. Clutch because the shot went in, and the game
              shifted at that point.

              My gut feeling is that pro players in big games do not just stampede
              to defeat because of a short run of points by the other team; and if
              this does happen, it isn't really to the credit of someone making a
              few consecutive shots.

              I rather like the simple idea that all contributions are equal: a
              backup forward who gets 5 good minutes in the 2nd quarter, by
              outplaying his counterpart, may not get a chance in the 4th quarter.
              Nevertheless, if he recognizes and performs his job, he was clutch.

              When someone says "yo, Reggie choked", and I say "He made 10 of 15
              shots", I might then hear "yes, but he missed when it counted".
              This to me is nonsense; if Reggie hadn't made 10 of 14, then shot #15
              wouldn't have mattered.
              And for the record, the shots that go in are the ones that count.

              When players do better in the postseason than in the regular season,
              they could be said to be clutch. Bill Russell, Michael Jordan,
              Hakeem Olajuwon always did better in the postseason, in spite of
              increased competition and pressure. Wilt, Oscar, and the Mailman
              always did worse.

              By this standard, "clutch" is certainly measurable. But there will
              always be a place for opinion.
            • NYFan@aol.com
              Just a note... I always like to do a very simple comparison; playoff numbers vs. regular season numbers. If a player can put up the same numbers, or better
              Message 6 of 19 , Nov 30, 2001
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                Just a note... I always like to do a very simple comparison; playoff numbers
                vs. regular season numbers. If a player can put up the same numbers, or
                better he's clutch. Generally I allow about 5% difference for FG% because
                'FG%s go wayyyy down.

                ~Ray
              • John Maxwell
                ... Actually, at least in baseball and the WNBA, this has not proven to be the case. Generally speaking, bad teams win more close games than any other kind and
                Message 7 of 19 , Nov 30, 2001
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                  > "Clutch" play by a team is often reflected in the percent of close
                  > games they win.

                  Actually, at least in baseball and the WNBA, this has not proven to be the
                  case. Generally speaking, bad teams win more close games than any other kind
                  and good teams win fewer close games than any other kind.

                  The reason is that bad teams don't win too many games that aren't close. Bad
                  teams, pretty much by definition, aren't going to blow out better teams.
                  They're going to play over their heads, while the better team plays below
                  expectations resulting in a close win for the bad team.

                  Good teams, on the other hand, routinely blow out their opponents, and
                  engage in many mor eblot-outs throughout the course of a season than close
                  games.

                  This is not to say that bad teams have better winning percentages in close
                  games than good teams, although there are a number of instances where this
                  is the case. Last season in the WNBA, the Detroit Shock was 10-22 and 4-4 in
                  games decided by three points or less. Sacramento was 20-12 and 3-3 in games
                  decided by three points or less. Drawing the conclusion that both Detroit
                  and Sacramento are both equally good teams "in the clutch" is inapproriate,
                  in my opinion. Or, if it does mean that both teams are equally good "in the
                  clutch," then perhaps having a good clutch team isn't all that important.

                  Also in the WNBA last season, the Portland Fire were 10-22 on the year, but
                  4-1 in overtime games. I would imagine that overtime is a decent barometer
                  of the "pucker factor" in the regular season, but if Portland is such a
                  clutch team, why are they only 10-22? On the other hand, Charlotte finished
                  18-14 on the season and advanced to the WNBA Finals but was 0-3 in overtime
                  games during the regular season.

                  I'm obviously cherry-picking here, but to say that "clutch" play by a team
                  is often reflected in the percent of close games it wins isn't supported by
                  the facts.

                  Here's a link to a baseball study on the issue
                  http://www.baseballstuff.com/btf/scholars/ruane/articles/onerun.htm

                  With regards to players, I looked at Yolanda Griffith and Lisa Leslie last
                  year and how they performed "in the clutch" to back up my opinion that
                  Griffith, and not Leslie, should have been the league MVP. I defined "in the
                  clutch" as being any time in the last 5 minutes of a game where the teams
                  were separated by no more than 5 points. Admittedly it was pretty arbitrary,
                  but as you all have discussed, defining "clutch" performance is one of the
                  larger stumbling block to determining if the ability to perform "in the
                  clutch" exists.

                  The first item of note from my study was that out of a possible 185 minutes
                  for Leslie and 190 for Griffith each played just shy of 80 minutes worth of
                  "clutch" time. That's two full games in the WNBA. Is that enough of a sample
                  size, 80 minutes, to be able to determine a player's ability "in the
                  clutch?" I don't know.

                  Anyway, Leslie's field goal percentage dropped 140 points "in the clutch"
                  while Griffith dropped 174. Leslie's rebound average dropped by two, her
                  assist and blocked shots averages were down slightly, while she increased
                  her steals average by half a point and decreased her turnover rate from 3.1
                  to 1.7. Her scoring average decreased by two. She doubled her trips to the
                  free throw line, but her percentage fell 100 points.

                  Griffith's rebound average dropped half a board, her assists remained
                  constant, she blocked no shots during this time (blocking 37 during the rest
                  of the season) while she picked up an extra half of a steal and decreased
                  her turnover rate from 2.34 to 1.28. Her scoring average decreased by more
                  than five points. Her trips to the free throw line decreased slightly, but
                  she hit essentially the same percentage.

                  So what does that all mean? I haven't a clue. My gut tells me that the
                  sample size is just too small to mean anything with regards to most of these
                  numbers. And while the drop in field goal percentage is alarming, it may
                  simply have to do with a difference in the way the opposition is defending
                  these two players. Then again, it might be because these two, as go-to
                  players, expect to take the shots at the end of the game and tend to force
                  them as a result.

                  I forget if it was Bill James, Rob Neyer or another Sabermetrician who did a
                  study into "clutch" hitting in baseball players using whatever the
                  definition in is for close and late situations -- something like after the
                  7th inning down two runs or less. What they found during the years they
                  studied (1980s) was players like Dane Iorg throughout the top-ten in batting
                  average in these situations. They also found that there was no consistency
                  with regards to these batting averages from year to year, leading them to
                  conclude that "clutch" hitting was not an actual ability. While "clutch"
                  performances exist, the idea that players have the ability to consistently
                  perform above expectations "in the clutch" has yet to be proven.

                  Just found the Rob Neyer article about which I was thinking.
                  http://www.diamond-mind.com/articles/neyerclutch.htm

                  John Maxwell
                  Director of Public Relations
                  Charlotte Sting
                • Ed Weiland
                  ... Not only that, baseball teams with a good winning pct. in one-run games generally decline the following season (as myself and several other White Sox fans
                  Message 8 of 19 , Dec 1, 2001
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                    --- John Maxwell <John.Maxwell@...>
                    wrote:
                    > > "Clutch" play by a team is often reflected in the
                    > percent of close
                    > > games they win.
                    >
                    > Actually, at least in baseball and the WNBA, this
                    > has not proven to be the
                    > case. Generally speaking, bad teams win more close
                    > games than any other kind
                    > and good teams win fewer close games than any other
                    > kind.
                    >
                    Not only that, baseball teams with a good winning pct.
                    in one-run games generally decline the following
                    season (as myself and several other White Sox fans
                    found out this past summer). I suspect the same is
                    true in the NBA, though I have never looked at the
                    subject, nor am I aware of anyone who has.

                    I would have no idea how to analyze which players are
                    clutch and which ones aren't. Basketball isn't like
                    baseball where you can just look at what each player
                    does in each AB and go from there. In basketball
                    there's defense, rebounding and passing going on in
                    addition to shooting. Those things would have to be
                    looked at also, once clutch situations were defined.

                    I've always felt "clutch" was one of those terms
                    people used to describe players they wanted to like.
                    Jerry West was called Mr. Clutch, despite being on the
                    losing team in eight NBA finals and winning only once.
                    This isn't to say West wasn't a clutch player. I just
                    wonder why West got tagged with Mr. Clutch, when it
                    was Bill Russell who was the biggest winner of that
                    time. Probably a racial thing.


                    Ed


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                  • Michael K. Tamada
                    ... I agree 100% with the statements that it is a mistake to look at teams records in close games, and to try to call the ones with good records clutch . A
                    Message 9 of 19 , Dec 1, 2001
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                      On Sat, 1 Dec 2001, Ed Weiland wrote:

                      > Not only that, baseball teams with a good winning pct.
                      > in one-run games generally decline the following
                      > season (as myself and several other White Sox fans
                      > found out this past summer). I suspect the same is
                      > true in the NBA, though I have never looked at the
                      > subject, nor am I aware of anyone who has.

                      I agree 100% with the statements that it is a mistake to look at teams'
                      records in close games, and to try to call the ones with good records
                      "clutch".

                      A minor quibble with the argument above however: while it is indeed true
                      that baseball teams with a good winning pct. in one-run games can be
                      expected to decline the following season, the same is true of ANY team in
                      ANY sport in ANY sort of games. Bill James many years ago thought he'd
                      discovered some profound truth in this and dreamt up some corny name for
                      it -- "The Law of Elastic Reboound" or something -- but it's been known
                      for about a century in statistics as "regression to the mean".

                      A team which wins 72 games in an NBA season is EXTREMELY likely to have
                      fewer wins the following season. A team which wins 90% of its 1-point
                      games in a season is extremely likely to win a lower percentage the
                      following season. A team which wins 70% of its 1-point games is very
                      likely to win a lower pct. the following season. Etc.

                      So while I agree 100% with both of the statements (that teams' 1-run
                      records have little meaning, except of course to contribute to their
                      win-loss record; and that teams with good 1-run records are likely to see
                      a decline in those records the following season), it is not the case that
                      the latter statement is evidence in favor of the former statement.

                      > I would have no idea how to analyze which players are
                      > clutch and which ones aren't. Basketball isn't like
                      > baseball where you can just look at what each player
                      > does in each AB and go from there. In basketball
                      > there's defense, rebounding and passing going on in
                      > addition to shooting. Those things would have to be
                      > looked at also, once clutch situations were defined.

                      True enough if we're looking for "total clutchness" but most of the NBA
                      players who are known as clutch are known for being clutch as shooters
                      during crunch time. Maybe once in a very long while they'll get a
                      reputation for good D in crunch time (Havlicek steals the ball, Bird
                      steals the ball), and I can't think of a single player who had a
                      reputation as a clutch rebounder. Maybe, say, Wilt, Russell, Silas, et
                      al -- but they were simply known as great rebounders period, it's not as
                      if people thought they only grabbed rebounds during crunch time and
                      lollygagged the rest of the game.

                      So to look for clutch players, I think it's an easy step to limit the
                      search to being a search for clutch *shooters*, and that is a more
                      limited, specific, easy-to-define concept.

                      > I've always felt "clutch" was one of those terms
                      > people used to describe players they wanted to like.
                      > Jerry West was called Mr. Clutch, despite being on the
                      > losing team in eight NBA finals and winning only once.
                      > This isn't to say West wasn't a clutch player. I just
                      > wonder why West got tagged with Mr. Clutch, when it
                      > was Bill Russell who was the biggest winner of that
                      > time. Probably a racial thing.

                      I agree with this also, although I would add the following hypothesis:
                      some players are given (or demand) the ball a lot in clutch situations.
                      And they thus shoot a lot of those crucial shots. I have no idea if some
                      players have a systematically higher probability of making those shots,
                      but if they take enough of them, some of them will go in. And people will
                      remember those, and tend to forget the shots that they missed. And that
                      will lead to the player getting a clutch reputation.

                      E.g. maybe Jerry West shot in his career 100 clutch shots, and made 47 of
                      them. That'd be identical to his career shooting percentage (both regular
                      season and playoff). So unless there's a tendency for clutch shots to
                      have a lower percentage overall (which actually might be the case), Jerry
                      West shot no better in clutch situations than in non-clutch. But
                      sportswriters, fans, and coaches would remember those 47 clutch shots
                      made, whereas maybe Wilt only made 15 and Gail Goodrich only made 8, and
                      thus Jerry West would get the reputation as Mr. Clutch.

                      I would add that the notion that Mike Goodman and others have advocated,
                      of looking at playoff games as clutch situations, is I think a good one,
                      and the fact that West's FG% was as high in the playoffs as it was in the
                      regular season is in itself a fairly remarkable, one might even say
                      clutch, performance. Especially given that his scoring per game INCRASED.


                      --MKT
                    • Ed Weiland
                      ... Increased shooting could also be a case of a player trying to shoulder too much of the load. It s interesting that in West s case the season his team
                      Message 10 of 19 , Dec 2, 2001
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                        --- "Michael K. Tamada" <tamada@...> wrote:
                        >
                        > I would add that the notion that Mike Goodman and
                        > others have advocated,
                        > of looking at playoff games as clutch situations, is
                        > I think a good one,
                        > and the fact that West's FG% was as high in the
                        > playoffs as it was in the
                        > regular season is in itself a fairly remarkable, one
                        > might even say
                        > clutch, performance. Especially given that his
                        > scoring per game INCRASED.


                        Increased shooting could also be a case of a player
                        trying to shoulder too much of the load. It's
                        interesting that in West's case the season his team
                        finally broke through and won the championship, 1972,
                        was the only year he averaged fewer points in the
                        playoffs than the regular season. West also shot only
                        .376 during the 1972 playoffs, by far the worst
                        showing of his career. He did post a career playoff
                        high in assists per game during the '72 playoffs.

                        Here are some other championship performances:

                        Wilt in '67 averaged a then career-low 21.7 ppg in the
                        playoffs, shot 104 points below his regular season FG
                        pct. (albeit a more-than-adequate .579), but posted a
                        career high with 9.0 assists per game.

                        Hakeem in '94 and '95 had FG pct. similar to his
                        regular season and career totals, but posted two of
                        his three highest playoff assist per game totals, 4.5
                        and 4.3 apg, both well above his career playoff
                        average of 3.3. Hakeem scored 33.0 ppg in the '95
                        playoffs, so it's not like he was sacrificing his
                        shots.

                        I'm not sure if the spike in assists is most
                        responsible for the championships, but I don't think
                        it can be ignored. Especially considering that star
                        players who aren't point guards, but possessed
                        good-to-great passing skills like Russell, Barry,
                        Walton, Bird and Jordan tended to win championships.
                        Sometimes the the most clutch thing for a player to do
                        is to get his teammates involved.

                        btw, I don't mean to knock West as non-clutch. HIs
                        Laker teams lost three game sevens to the Celtics by a
                        total of seven points. There had to be some bad luck
                        involved in all that.

                        Ed Weiland

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                      • harlanzo@yahoo.com
                        When considering clutch it seems weird to think about players actually improving over how they would in normal (nonpressure) situations. Rather, it seems to
                        Message 11 of 19 , Dec 2, 2001
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                          When considering clutch it seems weird to think about players
                          actually improving over how they would in normal (nonpressure)
                          situations. Rather, it seems to me that we might better define
                          clutch by looking at who did not become worse in clutch situations.
                          How you define clutch situations, incidentally, is a question I can't
                          really answer.

                          --- In APBR_analysis@y..., Ed Weiland <weiland1029@y...> wrote:
                          >
                          > --- "Michael K. Tamada" <tamada@o...> wrote:
                          > >
                          > > I would add that the notion that Mike Goodman and
                          > > others have advocated,
                          > > of looking at playoff games as clutch situations, is
                          > > I think a good one,
                          > > and the fact that West's FG% was as high in the
                          > > playoffs as it was in the
                          > > regular season is in itself a fairly remarkable, one
                          > > might even say
                          > > clutch, performance. Especially given that his
                          > > scoring per game INCRASED.
                          >
                          >
                          > Increased shooting could also be a case of a player
                          > trying to shoulder too much of the load. It's
                          > interesting that in West's case the season his team
                          > finally broke through and won the championship, 1972,
                          > was the only year he averaged fewer points in the
                          > playoffs than the regular season. West also shot only
                          > .376 during the 1972 playoffs, by far the worst
                          > showing of his career. He did post a career playoff
                          > high in assists per game during the '72 playoffs.
                          >
                          > Here are some other championship performances:
                          >
                          > Wilt in '67 averaged a then career-low 21.7 ppg in the
                          > playoffs, shot 104 points below his regular season FG
                          > pct. (albeit a more-than-adequate .579), but posted a
                          > career high with 9.0 assists per game.
                          >
                          > Hakeem in '94 and '95 had FG pct. similar to his
                          > regular season and career totals, but posted two of
                          > his three highest playoff assist per game totals, 4.5
                          > and 4.3 apg, both well above his career playoff
                          > average of 3.3. Hakeem scored 33.0 ppg in the '95
                          > playoffs, so it's not like he was sacrificing his
                          > shots.
                          >
                          > I'm not sure if the spike in assists is most
                          > responsible for the championships, but I don't think
                          > it can be ignored. Especially considering that star
                          > players who aren't point guards, but possessed
                          > good-to-great passing skills like Russell, Barry,
                          > Walton, Bird and Jordan tended to win championships.
                          > Sometimes the the most clutch thing for a player to do
                          > is to get his teammates involved.
                          >
                          > btw, I don't mean to knock West as non-clutch. HIs
                          > Laker teams lost three game sevens to the Celtics by a
                          > total of seven points. There had to be some bad luck
                          > involved in all that.
                          >
                          > Ed Weiland
                          >
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                        • igor eduardo küpfer
                          ... Here s the correlation matrix. (I hope it formats ok.) Days Dist Home MatchupP Dist 0.076 0.000 Home 0.173 -0.249 0.000 0.000
                          Message 12 of 19 , May 30, 2004
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                            Dean Oliver wrote:
                            > Ed --
                            >
                            > Nice. Is there correlation between variables? One that is key to
                            > understand is whether distance from previous game and days off are
                            > correlated. A home stand could be hiding some aspect of time off
                            > between games.

                            Here's the correlation matrix. (I hope it formats ok.)

                            Days Dist Home MatchupP
                            Dist 0.076
                            0.000

                            Home 0.173 -0.249
                            0.000 0.000

                            MatchupP -0.014 0.021 0.001
                            0.509 0.315 0.975

                            PtsDiff 0.060 -0.041 0.226 0.465
                            0.003 0.048 0.000 0.000

                            Cell Contents: Pearson correlation
                            P-Value

                            The correlations are generally pretty low.

                            > (Something also irks me about the p_win variable being
                            > endogenous.)
                            >

                            I'm not quite sure what endogenous means. If it means being related to the
                            other variables, I'm not quite sure if that's true: the matchup probability
                            calculation uses only team winning percentage and opponent winning
                            percentage, neither of which have any relationship to the other variables.
                            Maybe I misunderstood.


                            > I know I did a study of time off between games and saw that there is
                            > an optimal period of time off (more than 2 wasn't good, but neither
                            > was 0). That would imply a squared term in days off. But I didn't do
                            > it as rigorously as you did.
                            >

                            I did something like that, too. I can't remember which season I used, but I
                            found that most wins came on 2 day rests (I think). However, I didn't
                            include any other variables, so I could have just been looking at a
                            scheduling quirk for that season. I'll probably rerun this study on another
                            season to see if the results hold. If anyone else wants to give it a shot,
                            here's a table showing travel distances between NBA cities:

                            http://members.rogers.com/brothered/junk/TravelDistances.htm

                            ed
                          • Dean Oliver
                            ... Yeah, pretty low. Probably not much to worry about. ... to the ... probability ... variables. ... Basically, I assume you use 2004 win-loss records to
                            Message 13 of 19 , May 30, 2004
                            • 0 Attachment
                              --- In APBR_analysis@yahoogroups.com, igor eduardo küpfer
                              <edkupfer@r...> wrote:
                              > Dean Oliver wrote:
                              > > Ed --
                              > >
                              > > Nice. Is there correlation between variables? One that is key to
                              > > understand is whether distance from previous game and days off are
                              > > correlated. A home stand could be hiding some aspect of time off
                              > > between games.
                              >
                              > Here's the correlation matrix. (I hope it formats ok.)
                              >
                              > Days Dist Home MatchupP
                              > Dist 0.076
                              > 0.000
                              >
                              > Home 0.173 -0.249
                              > 0.000 0.000
                              >
                              > MatchupP -0.014 0.021 0.001
                              > 0.509 0.315 0.975
                              >
                              > PtsDiff 0.060 -0.041 0.226 0.465
                              > 0.003 0.048 0.000 0.000
                              >
                              > Cell Contents: Pearson correlation
                              > P-Value
                              >
                              > The correlations are generally pretty low.
                              >

                              Yeah, pretty low. Probably not much to worry about.

                              > > (Something also irks me about the p_win variable being
                              > > endogenous.)
                              > >
                              >
                              > I'm not quite sure what endogenous means. If it means being related
                              to the
                              > other variables, I'm not quite sure if that's true: the matchup
                              probability
                              > calculation uses only team winning percentage and opponent winning
                              > percentage, neither of which have any relationship to the other
                              variables.
                              > Maybe I misunderstood.

                              Basically, I assume you use 2004 win-loss records to evaluate p_win.
                              Well, those win-loss records are built from the things you are looking
                              at -- whether a team is at home or on the road, how many days off,
                              their whole schedule. Maybe the win-loss records of teams prior to
                              the matchup of the game you're looking at is exogenous (known a
                              priori). i.e., San Antonio faces the Lakers when one team is 12-5 and
                              the other is 10-7 -- use those records rather than their end of season
                              records. Maybe that's what you're doing, I dunno. I have doubt that
                              it would make a significant difference.


                              >
                              >
                              > > I know I did a study of time off between games and saw that there is
                              > > an optimal period of time off (more than 2 wasn't good, but neither
                              > > was 0). That would imply a squared term in days off. But I didn't do
                              > > it as rigorously as you did.
                              > >
                              >
                              > I did something like that, too. I can't remember which season I
                              used, but I
                              > found that most wins came on 2 day rests (I think). However, I didn't
                              > include any other variables, so I could have just been looking at a
                              > scheduling quirk for that season. I'll probably rerun this study on
                              another
                              > season to see if the results hold.

                              Just include the variable Days^2 in your regression and rerun that.
                              See what comes out significant.

                              DeanO

                              Dean Oliver
                              Author, Basketball on Paper
                              http://www.basketballonpaper.com
                              "Oliver goes beyond stats to dissect what it takes to win. His breezy
                              style makes for enjoyable reading, but there are plenty of points of
                              wisdom as well. This book can be appreciated by fans, players,
                              coaches and executives, but more importantly it can be used as a text
                              book for all these groups. You are sure to learn something you didn't
                              know about basketball here." Pete Palmer, co-author, Hidden Game of
                              Baseball and Hidden Game of Football
                            • igor eduardo küpfer
                              Dean Oliver wrote: ... Ah. I will try to use contemporary win/loss records in my next analysis. ... You ll have to help me out here, as I don t
                              Message 14 of 19 , May 30, 2004
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                                Dean Oliver wrote:
                                <snip>

                                >>>
                                >>
                                >> I'm not quite sure what endogenous means. If it means being related
                                > to the
                                >> other variables, I'm not quite sure if that's true: the matchup
                                >> probability calculation uses only team winning percentage and
                                >> opponent winning percentage, neither of which have any relationship
                                >> to the other variables. Maybe I misunderstood.
                                >
                                > Basically, I assume you use 2004 win-loss records to evaluate p_win.
                                > Well, those win-loss records are built from the things you are looking
                                > at -- whether a team is at home or on the road, how many days off,
                                > their whole schedule. Maybe the win-loss records of teams prior to
                                > the matchup of the game you're looking at is exogenous (known a
                                > priori). i.e., San Antonio faces the Lakers when one team is 12-5 and
                                > the other is 10-7 -- use those records rather than their end of season
                                > records. Maybe that's what you're doing, I dunno. I have doubt that
                                > it would make a significant difference.

                                Ah. I will try to use contemporary win/loss records in my next analysis.

                                <snip>

                                > Just include the variable Days^2 in your regression and rerun that.
                                > See what comes out significant.
                                >

                                You'll have to help me out here, as I don't know anything about transforming
                                data. Do you mean include Days^2 in addition to Days or instead of Days? I
                                did both, and here's how they turned out:

                                PtsDiff = - 21.2 + 2.59 Days +0.000021 Dist + 5.59 Home + 30.0 MatchupP -
                                0.394 Days_2

                                Predictor Coef SE Coef T P
                                Constant -21.169 1.195 -17.71 0.000
                                Days 2.5924 0.7956 3.26 0.001
                                Dist 0.0000214 0.0003555 0.06 0.952
                                Home 5.5937 0.4858 11.51 0.000
                                MatchupP 29.991 1.134 26.45 0.000
                                Days_2 -0.3938 0.1350 -2.92 0.004

                                PtsDiff = - 18.1 +0.000099 Dist + 5.87 Home + 29.9 MatchupP + 0.0236 Days_2

                                Predictor Coef SE Coef T P
                                Constant -18.0811 0.7304 -24.76 0.000
                                Dist 0.0000990 0.0003555 0.28 0.781
                                Home 5.8677 0.4794 12.24 0.000
                                MatchupP 29.890 1.136 26.32 0.000
                                Days_2 0.02362 0.04275 0.55 0.581


                                ed
                              • Dean Oliver
                                ... I should note that, not being an economist, I like throwing this word around without as great an appreciation or understanding for it as I should. ...
                                Message 15 of 19 , May 30, 2004
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                                  --- In APBR_analysis@yahoogroups.com, igor eduardo küpfer
                                  <edkupfer@r...> wrote:
                                  > >> I'm not quite sure what endogenous means. If it means being

                                  I should note that, not being an economist, I like throwing this word
                                  around without as great an appreciation or understanding for it as I
                                  should.


                                  > You'll have to help me out here, as I don't know anything about
                                  transforming
                                  > data. Do you mean include Days^2 in addition to Days or instead of
                                  Days? I
                                  > did both, and here's how they turned out:

                                  Include both, which you did in the first set below. Looks like it got
                                  you significant on both days and days^2. And the signs are as
                                  expected. It suggests optimal rest at about 3 days, longer than the 2
                                  days we saw before. (Potentially important for the talk about rust vs
                                  rest, esp if the Lakers wrap up on M.) Let me also ask -- is Days = 0
                                  if a team plays back to back nights or is that Days = 1?

                                  I'm sure there are other ways to manipulate things, but this looks
                                  like a pretty good thing. I'm saving it.

                                  Home is a binary 1/0 indicator for home/road, resp?

                                  >
                                  > PtsDiff = - 21.2 + 2.59 Days +0.000021 Dist + 5.59 Home + 30.0
                                  MatchupP -
                                  > 0.394 Days_2
                                  >
                                  > Predictor Coef SE Coef T P
                                  > Constant -21.169 1.195 -17.71 0.000
                                  > Days 2.5924 0.7956 3.26 0.001
                                  > Dist 0.0000214 0.0003555 0.06 0.952
                                  > Home 5.5937 0.4858 11.51 0.000
                                  > MatchupP 29.991 1.134 26.45 0.000
                                  > Days_2 -0.3938 0.1350 -2.92 0.004
                                  >
                                  >

                                  DeanO

                                  Dean Oliver
                                  Author, Basketball on Paper
                                  http://www.basketballonpaper.com
                                  "Dean Oliver looks at basketball with a fresh perspective. If you
                                  want a new way to analyze the game, this book is for you. You'll
                                  never watch a game the same way again. We use his stuff and it helps
                                  us." Yvan Kelly, Scout, Seattle Sonics
                                • igor eduardo küpfer
                                  Okay, I ran the test again, this time using 03-04 results. Before I show you what I got, let me address a couple of things. ... Hell, that s nothing. Once
                                  Message 16 of 19 , May 31, 2004
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                                    Okay, I ran the test again, this time using 03-04 results. Before I show you
                                    what I got, let me address a couple of things.

                                    Dean Oliver wrote:
                                    > --- In APBR_analysis@yahoogroups.com, igor eduardo küpfer
                                    > <edkupfer@r...> wrote:
                                    >>>> I'm not quite sure what endogenous means. If it means being
                                    >
                                    > I should note that, not being an economist, I like throwing this word
                                    > around without as great an appreciation or understanding for it as I
                                    > should.

                                    Hell, that's nothing. Once during the course of an argument with an
                                    ex-girlfriend I used the word "heretofore." I still don't know what it
                                    means.

                                    >
                                    >> You'll have to help me out here, as I don't know anything about
                                    >> transforming data. Do you mean include Days^2 in addition to Days or
                                    >> instead of Days? I did both, and here's how they turned out:
                                    >
                                    > Include both, which you did in the first set below. Looks like it got
                                    > you significant on both days and days^2. And the signs are as
                                    > expected. It suggests optimal rest at about 3 days, longer than the 2
                                    > days we saw before. (Potentially important for the talk about rust vs
                                    > rest, esp if the Lakers wrap up on M.)

                                    Questions: I don't understand a couple of things about the squared term. How
                                    did you know that squaring the Days variable would give a better fit? And,
                                    just exactly how does it suggest the optimal 3 day rest?

                                    > Let me also ask -- is Days = 0
                                    > if a team plays back to back nights or is that Days = 1?
                                    >

                                    The latter. I am subtracting game dates from each other.

                                    > I'm sure there are other ways to manipulate things, but this looks
                                    > like a pretty good thing. I'm saving it.
                                    >
                                    > Home is a binary 1/0 indicator for home/road, resp?

                                    Yes.

                                    Okay. Here are the results for 03-04. For the Matchup Probability, I used
                                    the team records heading into the game. For example, for two teams playing
                                    their first games of the season, I would use 0-0 records for each team in my
                                    probability calculation. Interestingly, this doesn't seem to affect the
                                    regression results too much. The effect of Days between games is reduced in
                                    this sample. Weird.


                                    PtsDiff = - 13.6 + 7.31 Home +0.000027 Distance + 18.1 WinProb + 0.722
                                    Days - 0.122 Days^2

                                    Predictor Coef SE Coef T P
                                    Constant -13.582 1.173 -11.58 0.000
                                    Home 7.3056 0.5010 14.58 0.000
                                    Distance 0.0000269 0.0003734 0.07 0.943
                                    WinProb 18.054 1.163 15.53 0.000
                                    Days 0.7221 0.7202 1.00 0.316
                                    Days^2 -0.1216 0.1138 -1.07 0.286

                                    S = 11.48 R-Sq = 16.7% R-Sq(adj) = 16.6%

                                    Analysis of Variance

                                    Source DF SS MS F P
                                    Regression 5 62072 12414 94.19 0.000
                                    Residual Error 2343 308806 132
                                    Total 2348 370877

                                    ed
                                  • Dean Oliver
                                    ... show you ... I ve had those moments, often inspired by arguments with soon-to-be ex-girlfriends. What the hell is vis-a-vis ? ... term. How ... fit? And,
                                    Message 17 of 19 , May 31, 2004
                                    • 0 Attachment
                                      --- In APBR_analysis@yahoogroups.com, igor eduardo küpfer
                                      <edkupfer@r...> wrote:
                                      > Okay, I ran the test again, this time using 03-04 results. Before I
                                      show you
                                      > what I got, let me address a couple of things.
                                      >
                                      > Dean Oliver wrote:
                                      > > --- In APBR_analysis@yahoogroups.com, igor eduardo küpfer
                                      > > <edkupfer@r...> wrote:
                                      > >>>> I'm not quite sure what endogenous means. If it means being
                                      > >
                                      > > I should note that, not being an economist, I like throwing this word
                                      > > around without as great an appreciation or understanding for it as I
                                      > > should.
                                      >
                                      > Hell, that's nothing. Once during the course of an argument with an
                                      > ex-girlfriend I used the word "heretofore." I still don't know what it
                                      > means.

                                      I've had those moments, often inspired by arguments with soon-to-be
                                      ex-girlfriends. What the hell is "vis-a-vis"?

                                      > >
                                      > >> You'll have to help me out here, as I don't know anything about
                                      > >> transforming data. Do you mean include Days^2 in addition to Days or
                                      > >> instead of Days? I did both, and here's how they turned out:
                                      > >
                                      > > Include both, which you did in the first set below. Looks like it got
                                      > > you significant on both days and days^2. And the signs are as
                                      > > expected. It suggests optimal rest at about 3 days, longer than the 2
                                      > > days we saw before. (Potentially important for the talk about rust vs
                                      > > rest, esp if the Lakers wrap up on M.)
                                      >
                                      > Questions: I don't understand a couple of things about the squared
                                      term. How
                                      > did you know that squaring the Days variable would give a better
                                      fit? And,
                                      > just exactly how does it suggest the optimal 3 day rest?

                                      I didn't _know_ it would give a better fit. I hoped it would because
                                      of what we were observing -- that there was an optimal number of days
                                      off. The only way to get an optimum out of a regression is to throw
                                      in higher order terms. Usually a squared term is plenty. It doesn't
                                      answer the bigger question of whether teams get rusty, though. It
                                      suggests an answer (another lesson in how to lie with statistics), one
                                      that I wouldn't trust from this study.

                                      Look at the results of your regression. Take just the Days and Days^2
                                      coefficients and calculate the marginal net points those terms
                                      contribute for Days = 1, 2, 3, 4, etc. You'll see a max at 3.

                                      >
                                      > > Let me also ask -- is Days = 0
                                      > > if a team plays back to back nights or is that Days = 1?
                                      > >
                                      >
                                      > The latter. I am subtracting game dates from each other.
                                      >

                                      So 2 days of rest is optimal.

                                      > > I'm sure there are other ways to manipulate things, but this looks
                                      > > like a pretty good thing. I'm saving it.
                                      > >
                                      > > Home is a binary 1/0 indicator for home/road, resp?
                                      >
                                      > Yes.
                                      >
                                      > Okay. Here are the results for 03-04. For the Matchup Probability, I
                                      used
                                      > the team records heading into the game. For example, for two teams
                                      playing
                                      > their first games of the season, I would use 0-0 records for each
                                      team in my
                                      > probability calculation.

                                      I was curious to see how you handled the early games of the season,
                                      especially the times where one team was undefeated. It looks like you
                                      used Pythagorean projections, rather than real records anyway. That
                                      helps. But 0-0 usually requires some other assumption, like a
                                      Bayesian prior that carries through the first few games.

                                      >Interestingly, this doesn't seem to affect the
                                      > regression results too much. The effect of Days between games is
                                      reduced in
                                      > this sample. Weird.

                                      Not sure what to make of that weakening of the Days. What was the R2
                                      of the previous version? We may have to improve the prior matchup P
                                      to get back a reasonable estimate of the value of Days. If you just
                                      look at games beyond the first 20 in the season, does r2 get better
                                      and does Days become more significant?

                                      >
                                      >
                                      > PtsDiff = - 13.6 + 7.31 Home +0.000027 Distance + 18.1 WinProb + 0.722
                                      > Days - 0.122 Days^2
                                      >
                                      > Predictor Coef SE Coef T P
                                      > Constant -13.582 1.173 -11.58 0.000
                                      > Home 7.3056 0.5010 14.58 0.000
                                      > Distance 0.0000269 0.0003734 0.07 0.943
                                      > WinProb 18.054 1.163 15.53 0.000
                                      > Days 0.7221 0.7202 1.00 0.316
                                      > Days^2 -0.1216 0.1138 -1.07 0.286
                                      >
                                      > S = 11.48 R-Sq = 16.7% R-Sq(adj) = 16.6%
                                      >
                                      > Analysis of Variance
                                      >
                                      > Source DF SS MS F P
                                      > Regression 5 62072 12414 94.19 0.000
                                      > Residual Error 2343 308806 132
                                      > Total 2348 370877
                                      >

                                      DeanO

                                      Dean Oliver
                                      Author, Basketball on Paper
                                      http://www.basketballonpaper.com
                                      "Excellent writing. There are a lot of math guys who just rush from
                                      the numbers to the conclusion. . .they'll tell you that Shaq is a real
                                      good player but his team would win a couple more games a year if he
                                      could hit a free throw. Dean is more than that; he's really
                                      struggling to understand the actual problem, rather than the
                                      statistical after-image of it. I learn a lot by reading him." Bill
                                      James, author Baseball Abstract
                                    • igor eduardo küpfer
                                      Replies to DanR and DeanO ... I m sorry I didn t make it clear. For the second analysis (on the 03-04 regular season results) I didn;t use Pythagorean records.
                                      Message 18 of 19 , Jun 2, 2004
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                                        Replies to DanR and DeanO

                                        Dean Oliver wrote:

                                        > I was curious to see how you handled the early games of the season,
                                        > especially the times where one team was undefeated. It looks like you
                                        > used Pythagorean projections, rather than real records anyway. That
                                        > helps. But 0-0 usually requires some other assumption, like a
                                        > Bayesian prior that carries through the first few games.

                                        I'm sorry I didn't make it clear. For the second analysis (on the 03-04
                                        regular season results) I didn;t use Pythagorean records. I instead used
                                        each team's record to date. Two teams facing each other on the first game of
                                        the season each had a 0.5 chance of winning that game, since they had
                                        identical 0-0 records.

                                        The results don't deviate much from my first analysis, which used season's
                                        end Pythagorean Win%. I supposed this is because after the first part of the
                                        season, each team's Pyth is relatively stable. I must admit to being a
                                        little surprised by this, though.

                                        > Not sure what to make of that weakening of the Days. What was the R2
                                        > of the previous version?

                                        r = 0.06 for 00-01, r = 0.03 for this season.

                                        > We may have to improve the prior matchup P
                                        > to get back a reasonable estimate of the value of Days. If you just
                                        > look at games beyond the first 20 in the season, does r2 get better
                                        > and does Days become more significant?
                                        >

                                        Games 2-20: r = 0.048 (p = 0.261)
                                        Games 21-82: r = 0.024 (p = 0.314

                                        dan_t_rosenbaum wrote:

                                        > Interesting results. Here are a couple of suggestions.
                                        >
                                        > I would leave out the MatchupP variable, since it is a lot like the
                                        > dependent variable. Including it probably increases R-squared a
                                        > lot, but probably doesn't do much else. (All in all, it probably is
                                        > pretty harmless, since it unlikely to be correlated with your
                                        > independent variables.)
                                        >
                                        > Another option with your day variable is to enter it as a series of
                                        > dummy variables.
                                        >
                                        > DAY0 - equals 1 if 0 days of rest, 0 otherwise
                                        > DAY1 - equals 1 if 1 day of rest , 0 otherwise
                                        > DAY2 - equals 1 if 2 days of rest, 0 otherwise
                                        > DAY3 - equals 1 if 3 days of rest, 0 otherwise
                                        > DAY4+ - equals 1 if 4 days or more of rest, 0 otherwise
                                        >
                                        > Then run the regression leaving one of those variables out.
                                        >
                                        > If, for example, you left DAY0 out of the regression, the DAY1
                                        > coefficient would give you the effect of playing on one day's rest
                                        > versus playing in a back-to-back.
                                        >
                                        > The DAY2 coefficent would give you the effect of playing on two
                                        > days' rest versus playing in a back-to-back.
                                        >
                                        > The DAY3 coefficent would give you the effect of playing on three
                                        > days' rest versus playing in a back-to-back.
                                        >
                                        > The DAY4+ coefficent would give you the effect of playing on four or
                                        > more days' rest versus playing in a back-to-back.
                                        >

                                        Okay, I tried this. The regression outputs follow. I'm afraid that I don't
                                        know how to interpret the results -- very few of the coefficients are
                                        significant. (Note that I use Day1 to mean 1 day between games, ie back to
                                        back -- the 1 does not mean "rest days.")

                                        Ommitting Days1

                                        Predictor Coef SE Coef T P
                                        Constant -3.8515 0.5971 -6.45 0.000
                                        Home 7.1257 0.5267 13.53 0.000
                                        Distance -0.0000105 0.0003920 -0.03 0.979
                                        Days2 0.5837 0.6157 0.95 0.343
                                        Days3 0.3022 0.7996 0.38 0.706
                                        Days4 -2.363 1.394 -1.70 0.090
                                        Days5+ 0.935 1.801 0.52 0.604


                                        Omitting Days2

                                        Predictor Coef SE Coef T P
                                        Constant -3.2678 0.5624 -5.81 0.000
                                        Home 7.1257 0.5267 13.53 0.000
                                        Distance -0.0000105 0.0003920 -0.03 0.979
                                        Days1 -0.5837 0.6157 -0.95 0.343
                                        Days3 -0.2815 0.6981 -0.40 0.687
                                        Days4 -2.947 1.338 -2.20 0.028
                                        Days5+ 0.351 1.758 0.20 0.842


                                        Omitting Days3

                                        Predictor Coef SE Coef T P
                                        Constant -3.5494 0.7833 -4.53 0.000
                                        Home 7.1257 0.5267 13.53 0.000
                                        Distance -0.0000105 0.0003920 -0.03 0.979
                                        Days1 -0.3022 0.7996 -0.38 0.706
                                        Days2 0.2815 0.6981 0.40 0.687
                                        Days4 -2.665 1.426 -1.87 0.062
                                        Days5+ 0.633 1.824 0.35 0.729


                                        Omitting Days4

                                        Predictor Coef SE Coef T P
                                        Constant -6.215 1.385 -4.49 0.000
                                        Home 7.1257 0.5267 13.53 0.000
                                        Distance -0.0000105 0.0003920 -0.03 0.979
                                        Days1 2.363 1.394 1.70 0.090
                                        Days2 2.947 1.338 2.20 0.028
                                        Days3 2.665 1.426 1.87 0.062
                                        Days5+ 3.298 2.152 1.53 0.126

                                        Omitting Days5+

                                        Predictor Coef SE Coef T P
                                        Constant -2.917 1.799 -1.62 0.105
                                        Home 7.1257 0.5267 13.53 0.000
                                        Distance -0.0000105 0.0003920 -0.03 0.979
                                        Days1 -0.935 1.801 -0.52 0.604
                                        Days2 -0.351 1.758 -0.20 0.842
                                        Days3 -0.633 1.824 -0.35 0.729
                                        Days4 -3.298 2.152 -1.53 0.126


                                        ed
                                      • igor eduardo küpfer
                                        ... http://www.shrpsports.com/nba/stand/2002.htm -- ed
                                        Message 19 of 19 , Nov 2, 2004
                                        • 0 Attachment
                                          ivan ivan wrote:
                                          > this is a simple question
                                          > but i can't find it anywhere....
                                          >
                                          >
                                          > I'm doing analysis on how a history of winning or losing affects your
                                          > chances of winning at the end of close games... so does anyone know
                                          > where i can standings for the 2001-2002 NBA season?
                                          > i want the home and away records?
                                          >

                                          http://www.shrpsports.com/nba/stand/2002.htm

                                          --
                                          ed
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